Overview

Dataset statistics

Number of variables39
Number of observations260601
Missing cells0
Missing cells (%)0.0%
Duplicate rows9749
Duplicate rows (%)3.7%
Total size in memory79.5 MiB
Average record size in memory320.0 B

Variable types

Numeric8
Categorical31

Alerts

Dataset has 9749 (3.7%) duplicate rowsDuplicates
land_surface_condition is highly imbalanced (51.3%)Imbalance
foundation_type is highly imbalanced (60.9%)Imbalance
ground_floor_type is highly imbalanced (59.3%)Imbalance
position is highly imbalanced (50.4%)Imbalance
plan_configuration is highly imbalanced (90.7%)Imbalance
has_superstructure_adobe_mud is highly imbalanced (56.8%)Imbalance
has_superstructure_stone_flag is highly imbalanced (78.4%)Imbalance
has_superstructure_cement_mortar_stone is highly imbalanced (86.9%)Imbalance
has_superstructure_mud_mortar_brick is highly imbalanced (64.1%)Imbalance
has_superstructure_cement_mortar_brick is highly imbalanced (61.5%)Imbalance
has_superstructure_bamboo is highly imbalanced (58.0%)Imbalance
has_superstructure_rc_non_engineered is highly imbalanced (74.6%)Imbalance
has_superstructure_rc_engineered is highly imbalanced (88.2%)Imbalance
has_superstructure_other is highly imbalanced (88.8%)Imbalance
legal_ownership_status is highly imbalanced (86.0%)Imbalance
has_secondary_use_agriculture is highly imbalanced (65.5%)Imbalance
has_secondary_use_hotel is highly imbalanced (78.8%)Imbalance
has_secondary_use_rental is highly imbalanced (93.2%)Imbalance
has_secondary_use_institution is highly imbalanced (98.9%)Imbalance
has_secondary_use_school is highly imbalanced (99.5%)Imbalance
has_secondary_use_industry is highly imbalanced (98.8%)Imbalance
has_secondary_use_health_post is highly imbalanced (99.7%)Imbalance
has_secondary_use_gov_office is highly imbalanced (99.8%)Imbalance
has_secondary_use_use_police is highly imbalanced (99.9%)Imbalance
has_secondary_use_other is highly imbalanced (95.4%)Imbalance
geo_level_1_id has 4011 (1.5%) zerosZeros
age has 26041 (10.0%) zerosZeros
count_families has 20862 (8.0%) zerosZeros

Reproduction

Analysis started2024-02-02 09:50:27.527000
Analysis finished2024-02-02 09:51:03.257763
Duration35.73 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

geo_level_1_id
Real number (ℝ)

ZEROS 

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.900353
Minimum0
Maximum30
Zeros4011
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-02T10:51:03.447480image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q17
median12
Q321
95-th percentile27
Maximum30
Range30
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.0336166
Coefficient of variation (CV)0.57794334
Kurtosis-1.2132488
Mean13.900353
Median Absolute Deviation (MAD)6
Skewness0.27253035
Sum3622446
Variance64.538996
MonotonicityNot monotonic
2024-02-02T10:51:03.660067image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
6 24381
 
9.4%
26 22615
 
8.7%
10 22079
 
8.5%
17 21813
 
8.4%
8 19080
 
7.3%
7 18994
 
7.3%
20 17216
 
6.6%
21 14889
 
5.7%
4 14568
 
5.6%
27 12532
 
4.8%
Other values (21) 72434
27.8%
ValueCountFrequency (%)
0 4011
 
1.5%
1 2701
 
1.0%
2 931
 
0.4%
3 7540
 
2.9%
4 14568
5.6%
5 2690
 
1.0%
6 24381
9.4%
7 18994
7.3%
8 19080
7.3%
9 3958
 
1.5%
ValueCountFrequency (%)
30 2686
 
1.0%
29 396
 
0.2%
28 265
 
0.1%
27 12532
4.8%
26 22615
8.7%
25 5624
 
2.2%
24 1310
 
0.5%
23 1121
 
0.4%
22 6252
 
2.4%
21 14889
5.7%

geo_level_2_id
Real number (ℝ)

Distinct1414
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean701.07469
Minimum0
Maximum1427
Zeros38
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-02T10:51:04.015337image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile69
Q1350
median702
Q31050
95-th percentile1377
Maximum1427
Range1427
Interquartile range (IQR)700

Descriptive statistics

Standard deviation412.71073
Coefficient of variation (CV)0.58868298
Kurtosis-1.1882325
Mean701.07469
Median Absolute Deviation (MAD)349
Skewness0.028957381
Sum1.8270076 × 108
Variance170330.15
MonotonicityNot monotonic
2024-02-02T10:51:04.394509image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 4038
 
1.5%
158 2520
 
1.0%
181 2080
 
0.8%
1387 2040
 
0.8%
157 1897
 
0.7%
363 1760
 
0.7%
463 1740
 
0.7%
673 1704
 
0.7%
533 1684
 
0.6%
883 1626
 
0.6%
Other values (1404) 239512
91.9%
ValueCountFrequency (%)
0 38
 
< 0.1%
1 204
0.1%
3 77
 
< 0.1%
4 315
0.1%
5 25
 
< 0.1%
6 2
 
< 0.1%
7 100
 
< 0.1%
8 120
 
< 0.1%
9 333
0.1%
10 354
0.1%
ValueCountFrequency (%)
1427 6
 
< 0.1%
1426 286
0.1%
1425 466
0.2%
1424 7
 
< 0.1%
1423 3
 
< 0.1%
1422 216
0.1%
1421 254
0.1%
1420 10
 
< 0.1%
1419 95
 
< 0.1%
1418 152
 
0.1%

geo_level_3_id
Real number (ℝ)

Distinct11595
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6257.8761
Minimum0
Maximum12567
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-02T10:51:04.748846image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile611
Q13073
median6270
Q39412
95-th percentile11927
Maximum12567
Range12567
Interquartile range (IQR)6339

Descriptive statistics

Standard deviation3646.3696
Coefficient of variation (CV)0.58268485
Kurtosis-1.2138965
Mean6257.8761
Median Absolute Deviation (MAD)3171
Skewness0.00039351209
Sum1.6308088 × 109
Variance13296012
MonotonicityNot monotonic
2024-02-02T10:51:05.073675image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
633 651
 
0.2%
9133 647
 
0.2%
621 530
 
0.2%
11246 470
 
0.2%
2005 466
 
0.2%
11440 455
 
0.2%
7723 443
 
0.2%
9229 381
 
0.1%
2452 349
 
0.1%
12258 312
 
0.1%
Other values (11585) 255897
98.2%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 6
 
< 0.1%
3 9
 
< 0.1%
5 14
 
< 0.1%
6 21
 
< 0.1%
7 2
 
< 0.1%
8 31
< 0.1%
9 3
 
< 0.1%
10 1
 
< 0.1%
11 62
< 0.1%
ValueCountFrequency (%)
12567 1
 
< 0.1%
12565 7
 
< 0.1%
12564 6
 
< 0.1%
12563 24
< 0.1%
12562 3
 
< 0.1%
12561 19
< 0.1%
12560 17
 
< 0.1%
12559 6
 
< 0.1%
12558 6
 
< 0.1%
12557 44
< 0.1%

count_floors_pre_eq
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1297232
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-02T10:51:05.267529image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile3
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.72766455
Coefficient of variation (CV)0.34167095
Kurtosis2.3225979
Mean2.1297232
Median Absolute Deviation (MAD)0
Skewness0.83411296
Sum555008
Variance0.52949569
MonotonicityNot monotonic
2024-02-02T10:51:05.485940image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 156623
60.1%
3 55617
 
21.3%
1 40441
 
15.5%
4 5424
 
2.1%
5 2246
 
0.9%
6 209
 
0.1%
7 39
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
1 40441
 
15.5%
2 156623
60.1%
3 55617
 
21.3%
4 5424
 
2.1%
5 2246
 
0.9%
6 209
 
0.1%
7 39
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 1
 
< 0.1%
7 39
 
< 0.1%
6 209
 
0.1%
5 2246
 
0.9%
4 5424
 
2.1%
3 55617
 
21.3%
2 156623
60.1%
1 40441
 
15.5%

age
Real number (ℝ)

ZEROS 

Distinct42
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.535029
Minimum0
Maximum995
Zeros26041
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-02T10:51:05.667775image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median15
Q330
95-th percentile60
Maximum995
Range995
Interquartile range (IQR)20

Descriptive statistics

Standard deviation73.565937
Coefficient of variation (CV)2.7724084
Kurtosis157.24824
Mean26.535029
Median Absolute Deviation (MAD)10
Skewness12.192494
Sum6915055
Variance5411.947
MonotonicityNot monotonic
2024-02-02T10:51:05.860270image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
10 38896
14.9%
15 36010
13.8%
5 33697
12.9%
20 32182
12.3%
0 26041
10.0%
25 24366
9.3%
30 18028
6.9%
35 10710
 
4.1%
40 10559
 
4.1%
50 7257
 
2.8%
Other values (32) 22855
8.8%
ValueCountFrequency (%)
0 26041
10.0%
5 33697
12.9%
10 38896
14.9%
15 36010
13.8%
20 32182
12.3%
25 24366
9.3%
30 18028
6.9%
35 10710
 
4.1%
40 10559
 
4.1%
45 4711
 
1.8%
ValueCountFrequency (%)
995 1390
0.5%
200 106
 
< 0.1%
195 2
 
< 0.1%
190 3
 
< 0.1%
185 1
 
< 0.1%
180 7
 
< 0.1%
175 5
 
< 0.1%
170 6
 
< 0.1%
165 2
 
< 0.1%
160 6
 
< 0.1%

area_percentage
Real number (ℝ)

Distinct84
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0180506
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-02T10:51:06.074125image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median7
Q39
95-th percentile16
Maximum100
Range99
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.3922309
Coefficient of variation (CV)0.54779287
Kurtosis30.438258
Mean8.0180506
Median Absolute Deviation (MAD)2
Skewness3.5260823
Sum2089512
Variance19.291693
MonotonicityNot monotonic
2024-02-02T10:51:06.277034image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 42013
16.1%
7 36752
14.1%
5 32724
12.6%
8 28445
10.9%
9 22199
8.5%
4 19236
7.4%
10 15613
 
6.0%
11 13907
 
5.3%
3 11837
 
4.5%
12 7581
 
2.9%
Other values (74) 30294
11.6%
ValueCountFrequency (%)
1 90
 
< 0.1%
2 3181
 
1.2%
3 11837
 
4.5%
4 19236
7.4%
5 32724
12.6%
6 42013
16.1%
7 36752
14.1%
8 28445
10.9%
9 22199
8.5%
10 15613
 
6.0%
ValueCountFrequency (%)
100 1
 
< 0.1%
96 3
< 0.1%
90 1
 
< 0.1%
86 5
< 0.1%
85 4
< 0.1%
84 3
< 0.1%
83 3
< 0.1%
82 1
 
< 0.1%
80 1
 
< 0.1%
78 1
 
< 0.1%

height_percentage
Real number (ℝ)

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4343652
Minimum2
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-02T10:51:06.507014image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median5
Q36
95-th percentile9
Maximum32
Range30
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9184182
Coefficient of variation (CV)0.35301607
Kurtosis14.318526
Mean5.4343652
Median Absolute Deviation (MAD)1
Skewness1.8082618
Sum1416201
Variance3.6803285
MonotonicityNot monotonic
2024-02-02T10:51:06.802069image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
5 78513
30.1%
6 46477
17.8%
4 37763
14.5%
7 35465
13.6%
3 25957
 
10.0%
8 13902
 
5.3%
2 9305
 
3.6%
9 5376
 
2.1%
10 4492
 
1.7%
11 917
 
0.4%
Other values (17) 2434
 
0.9%
ValueCountFrequency (%)
2 9305
 
3.6%
3 25957
 
10.0%
4 37763
14.5%
5 78513
30.1%
6 46477
17.8%
7 35465
13.6%
8 13902
 
5.3%
9 5376
 
2.1%
10 4492
 
1.7%
11 917
 
0.4%
ValueCountFrequency (%)
32 75
< 0.1%
31 1
 
< 0.1%
28 2
 
< 0.1%
26 2
 
< 0.1%
25 3
 
< 0.1%
24 4
 
< 0.1%
23 11
 
< 0.1%
21 13
 
< 0.1%
20 33
< 0.1%
19 7
 
< 0.1%

land_surface_condition
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
t
216757 
n
35528 
o
 
8316

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowt
2nd rowo
3rd rowt
4th rowt
5th rowt

Common Values

ValueCountFrequency (%)
t 216757
83.2%
n 35528
 
13.6%
o 8316
 
3.2%

Length

2024-02-02T10:51:07.024084image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-02T10:51:07.140337image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
t 216757
83.2%
n 35528
 
13.6%
o 8316
 
3.2%

Most occurring characters

ValueCountFrequency (%)
t 216757
83.2%
n 35528
 
13.6%
o 8316
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 260601
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 216757
83.2%
n 35528
 
13.6%
o 8316
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 260601
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 216757
83.2%
n 35528
 
13.6%
o 8316
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 216757
83.2%
n 35528
 
13.6%
o 8316
 
3.2%

foundation_type
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
r
219196 
w
 
15118
u
 
14260
i
 
10579
h
 
1448

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowr
2nd rowr
3rd rowr
4th rowr
5th rowr

Common Values

ValueCountFrequency (%)
r 219196
84.1%
w 15118
 
5.8%
u 14260
 
5.5%
i 10579
 
4.1%
h 1448
 
0.6%

Length

2024-02-02T10:51:07.401747image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-02T10:51:07.588769image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
r 219196
84.1%
w 15118
 
5.8%
u 14260
 
5.5%
i 10579
 
4.1%
h 1448
 
0.6%

Most occurring characters

ValueCountFrequency (%)
r 219196
84.1%
w 15118
 
5.8%
u 14260
 
5.5%
i 10579
 
4.1%
h 1448
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 260601
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 219196
84.1%
w 15118
 
5.8%
u 14260
 
5.5%
i 10579
 
4.1%
h 1448
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 260601
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 219196
84.1%
w 15118
 
5.8%
u 14260
 
5.5%
i 10579
 
4.1%
h 1448
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 219196
84.1%
w 15118
 
5.8%
u 14260
 
5.5%
i 10579
 
4.1%
h 1448
 
0.6%

roof_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
n
182842 
q
61576 
x
 
16183

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rown
2nd rown
3rd rown
4th rown
5th rown

Common Values

ValueCountFrequency (%)
n 182842
70.2%
q 61576
 
23.6%
x 16183
 
6.2%

Length

2024-02-02T10:51:07.757504image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-02T10:51:07.909139image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
n 182842
70.2%
q 61576
 
23.6%
x 16183
 
6.2%

Most occurring characters

ValueCountFrequency (%)
n 182842
70.2%
q 61576
 
23.6%
x 16183
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 260601
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 182842
70.2%
q 61576
 
23.6%
x 16183
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 260601
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 182842
70.2%
q 61576
 
23.6%
x 16183
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 182842
70.2%
q 61576
 
23.6%
x 16183
 
6.2%

ground_floor_type
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
f
209619 
x
24877 
v
24593 
z
 
1004
m
 
508

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowf
2nd rowx
3rd rowf
4th rowf
5th rowf

Common Values

ValueCountFrequency (%)
f 209619
80.4%
x 24877
 
9.5%
v 24593
 
9.4%
z 1004
 
0.4%
m 508
 
0.2%

Length

2024-02-02T10:51:08.057194image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-02T10:51:08.247915image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
f 209619
80.4%
x 24877
 
9.5%
v 24593
 
9.4%
z 1004
 
0.4%
m 508
 
0.2%

Most occurring characters

ValueCountFrequency (%)
f 209619
80.4%
x 24877
 
9.5%
v 24593
 
9.4%
z 1004
 
0.4%
m 508
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 260601
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 209619
80.4%
x 24877
 
9.5%
v 24593
 
9.4%
z 1004
 
0.4%
m 508
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 260601
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 209619
80.4%
x 24877
 
9.5%
v 24593
 
9.4%
z 1004
 
0.4%
m 508
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 209619
80.4%
x 24877
 
9.5%
v 24593
 
9.4%
z 1004
 
0.4%
m 508
 
0.2%

other_floor_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
q
165282 
x
43448 
j
39843 
s
 
12028

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowq
2nd rowq
3rd rowx
4th rowx
5th rowx

Common Values

ValueCountFrequency (%)
q 165282
63.4%
x 43448
 
16.7%
j 39843
 
15.3%
s 12028
 
4.6%

Length

2024-02-02T10:51:08.604599image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-02T10:51:08.891592image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
q 165282
63.4%
x 43448
 
16.7%
j 39843
 
15.3%
s 12028
 
4.6%

Most occurring characters

ValueCountFrequency (%)
q 165282
63.4%
x 43448
 
16.7%
j 39843
 
15.3%
s 12028
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 260601
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
q 165282
63.4%
x 43448
 
16.7%
j 39843
 
15.3%
s 12028
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 260601
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
q 165282
63.4%
x 43448
 
16.7%
j 39843
 
15.3%
s 12028
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
q 165282
63.4%
x 43448
 
16.7%
j 39843
 
15.3%
s 12028
 
4.6%

position
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
s
202090 
t
42896 
j
 
13282
o
 
2333

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowt
2nd rows
3rd rowt
4th rows
5th rows

Common Values

ValueCountFrequency (%)
s 202090
77.5%
t 42896
 
16.5%
j 13282
 
5.1%
o 2333
 
0.9%

Length

2024-02-02T10:51:09.172614image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-02T10:51:09.382723image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
s 202090
77.5%
t 42896
 
16.5%
j 13282
 
5.1%
o 2333
 
0.9%

Most occurring characters

ValueCountFrequency (%)
s 202090
77.5%
t 42896
 
16.5%
j 13282
 
5.1%
o 2333
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 260601
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 202090
77.5%
t 42896
 
16.5%
j 13282
 
5.1%
o 2333
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 260601
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 202090
77.5%
t 42896
 
16.5%
j 13282
 
5.1%
o 2333
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 202090
77.5%
t 42896
 
16.5%
j 13282
 
5.1%
o 2333
 
0.9%

plan_configuration
Categorical

IMBALANCE 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
d
250072 
q
 
5692
u
 
3649
s
 
346
c
 
325
Other values (5)
 
517

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowd
2nd rowd
3rd rowd
4th rowd
5th rowd

Common Values

ValueCountFrequency (%)
d 250072
96.0%
q 5692
 
2.2%
u 3649
 
1.4%
s 346
 
0.1%
c 325
 
0.1%
a 252
 
0.1%
o 159
 
0.1%
m 46
 
< 0.1%
n 38
 
< 0.1%
f 22
 
< 0.1%

Length

2024-02-02T10:51:09.502177image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-02T10:51:09.723392image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
d 250072
96.0%
q 5692
 
2.2%
u 3649
 
1.4%
s 346
 
0.1%
c 325
 
0.1%
a 252
 
0.1%
o 159
 
0.1%
m 46
 
< 0.1%
n 38
 
< 0.1%
f 22
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
d 250072
96.0%
q 5692
 
2.2%
u 3649
 
1.4%
s 346
 
0.1%
c 325
 
0.1%
a 252
 
0.1%
o 159
 
0.1%
m 46
 
< 0.1%
n 38
 
< 0.1%
f 22
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 260601
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 250072
96.0%
q 5692
 
2.2%
u 3649
 
1.4%
s 346
 
0.1%
c 325
 
0.1%
a 252
 
0.1%
o 159
 
0.1%
m 46
 
< 0.1%
n 38
 
< 0.1%
f 22
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 260601
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 250072
96.0%
q 5692
 
2.2%
u 3649
 
1.4%
s 346
 
0.1%
c 325
 
0.1%
a 252
 
0.1%
o 159
 
0.1%
m 46
 
< 0.1%
n 38
 
< 0.1%
f 22
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 250072
96.0%
q 5692
 
2.2%
u 3649
 
1.4%
s 346
 
0.1%
c 325
 
0.1%
a 252
 
0.1%
o 159
 
0.1%
m 46
 
< 0.1%
n 38
 
< 0.1%
f 22
 
< 0.1%

has_superstructure_adobe_mud
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
0
237500 
1
 
23101

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 237500
91.1%
1 23101
 
8.9%

Length

2024-02-02T10:51:09.913870image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-02T10:51:10.216311image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 237500
91.1%
1 23101
 
8.9%

Most occurring characters

ValueCountFrequency (%)
0 237500
91.1%
1 23101
 
8.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 237500
91.1%
1 23101
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 237500
91.1%
1 23101
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 237500
91.1%
1 23101
 
8.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
1
198561 
0
62040 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 198561
76.2%
0 62040
 
23.8%

Length

2024-02-02T10:51:10.362794image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-02T10:51:10.507411image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1 198561
76.2%
0 62040
 
23.8%

Most occurring characters

ValueCountFrequency (%)
1 198561
76.2%
0 62040
 
23.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 198561
76.2%
0 62040
 
23.8%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 198561
76.2%
0 62040
 
23.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 198561
76.2%
0 62040
 
23.8%

has_superstructure_stone_flag
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
0
251654 
1
 
8947

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 251654
96.6%
1 8947
 
3.4%

Length

2024-02-02T10:51:10.616879image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-02T10:51:10.719392image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 251654
96.6%
1 8947
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 251654
96.6%
1 8947
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 251654
96.6%
1 8947
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 251654
96.6%
1 8947
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 251654
96.6%
1 8947
 
3.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
0
255849 
1
 
4752

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 255849
98.2%
1 4752
 
1.8%

Length

2024-02-02T10:51:10.827108image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-02T10:51:10.926851image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 255849
98.2%
1 4752
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 255849
98.2%
1 4752
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 255849
98.2%
1 4752
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 255849
98.2%
1 4752
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 255849
98.2%
1 4752
 
1.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
0
242840 
1
 
17761

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 242840
93.2%
1 17761
 
6.8%

Length

2024-02-02T10:51:11.063472image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-02T10:51:11.240095image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 242840
93.2%
1 17761
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0 242840
93.2%
1 17761
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 242840
93.2%
1 17761
 
6.8%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 242840
93.2%
1 17761
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 242840
93.2%
1 17761
 
6.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
0
240986 
1
 
19615

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 240986
92.5%
1 19615
 
7.5%

Length

2024-02-02T10:51:11.349076image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-02T10:51:11.582595image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 240986
92.5%
1 19615
 
7.5%

Most occurring characters

ValueCountFrequency (%)
0 240986
92.5%
1 19615
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 240986
92.5%
1 19615
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 240986
92.5%
1 19615
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 240986
92.5%
1 19615
 
7.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
0
194151 
1
66450 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 194151
74.5%
1 66450
 
25.5%

Length

2024-02-02T10:51:11.832847image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-02T10:51:12.060466image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 194151
74.5%
1 66450
 
25.5%

Most occurring characters

ValueCountFrequency (%)
0 194151
74.5%
1 66450
 
25.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 194151
74.5%
1 66450
 
25.5%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 194151
74.5%
1 66450
 
25.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 194151
74.5%
1 66450
 
25.5%

has_superstructure_bamboo
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
0
238447 
1
 
22154

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 238447
91.5%
1 22154
 
8.5%

Length

2024-02-02T10:51:12.215685image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-02T10:51:12.349164image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 238447
91.5%
1 22154
 
8.5%

Most occurring characters

ValueCountFrequency (%)
0 238447
91.5%
1 22154
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 238447
91.5%
1 22154
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 238447
91.5%
1 22154
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 238447
91.5%
1 22154
 
8.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
0
249502 
1
 
11099

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 249502
95.7%
1 11099
 
4.3%

Length

2024-02-02T10:51:12.607202image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-02T10:51:12.844404image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 249502
95.7%
1 11099
 
4.3%

Most occurring characters

ValueCountFrequency (%)
0 249502
95.7%
1 11099
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 249502
95.7%
1 11099
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 249502
95.7%
1 11099
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 249502
95.7%
1 11099
 
4.3%

has_superstructure_rc_engineered
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
0
256468 
1
 
4133

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 256468
98.4%
1 4133
 
1.6%

Length

2024-02-02T10:51:12.989916image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-02T10:51:13.124571image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 256468
98.4%
1 4133
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 256468
98.4%
1 4133
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 256468
98.4%
1 4133
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 256468
98.4%
1 4133
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 256468
98.4%
1 4133
 
1.6%

has_superstructure_other
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
0
256696 
1
 
3905

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 256696
98.5%
1 3905
 
1.5%

Length

2024-02-02T10:51:13.255232image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-02T10:51:13.409186image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 256696
98.5%
1 3905
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 256696
98.5%
1 3905
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 256696
98.5%
1 3905
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 256696
98.5%
1 3905
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 256696
98.5%
1 3905
 
1.5%

legal_ownership_status
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
v
250939 
a
 
5512
w
 
2677
r
 
1473

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowv
2nd rowv
3rd rowv
4th rowv
5th rowv

Common Values

ValueCountFrequency (%)
v 250939
96.3%
a 5512
 
2.1%
w 2677
 
1.0%
r 1473
 
0.6%

Length

2024-02-02T10:51:13.723327image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-02T10:51:13.963474image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
v 250939
96.3%
a 5512
 
2.1%
w 2677
 
1.0%
r 1473
 
0.6%

Most occurring characters

ValueCountFrequency (%)
v 250939
96.3%
a 5512
 
2.1%
w 2677
 
1.0%
r 1473
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 260601
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
v 250939
96.3%
a 5512
 
2.1%
w 2677
 
1.0%
r 1473
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 260601
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
v 250939
96.3%
a 5512
 
2.1%
w 2677
 
1.0%
r 1473
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
v 250939
96.3%
a 5512
 
2.1%
w 2677
 
1.0%
r 1473
 
0.6%

count_families
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.98394864
Minimum0
Maximum9
Zeros20862
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size4.0 MiB
2024-02-02T10:51:14.240514image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile2
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.41838898
Coefficient of variation (CV)0.42521424
Kurtosis17.670943
Mean0.98394864
Median Absolute Deviation (MAD)0
Skewness1.6347579
Sum256418
Variance0.17504934
MonotonicityNot monotonic
2024-02-02T10:51:14.547034image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 226115
86.8%
0 20862
 
8.0%
2 11294
 
4.3%
3 1802
 
0.7%
4 389
 
0.1%
5 104
 
< 0.1%
6 22
 
< 0.1%
7 7
 
< 0.1%
9 4
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
0 20862
 
8.0%
1 226115
86.8%
2 11294
 
4.3%
3 1802
 
0.7%
4 389
 
0.1%
5 104
 
< 0.1%
6 22
 
< 0.1%
7 7
 
< 0.1%
8 2
 
< 0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
9 4
 
< 0.1%
8 2
 
< 0.1%
7 7
 
< 0.1%
6 22
 
< 0.1%
5 104
 
< 0.1%
4 389
 
0.1%
3 1802
 
0.7%
2 11294
 
4.3%
1 226115
86.8%
0 20862
 
8.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
0
231445 
1
29156 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 231445
88.8%
1 29156
 
11.2%

Length

2024-02-02T10:51:14.798817image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-02T10:51:15.084659image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 231445
88.8%
1 29156
 
11.2%

Most occurring characters

ValueCountFrequency (%)
0 231445
88.8%
1 29156
 
11.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 231445
88.8%
1 29156
 
11.2%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 231445
88.8%
1 29156
 
11.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 231445
88.8%
1 29156
 
11.2%

has_secondary_use_agriculture
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
0
243824 
1
 
16777

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 243824
93.6%
1 16777
 
6.4%

Length

2024-02-02T10:51:15.372575image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-02T10:51:15.504099image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 243824
93.6%
1 16777
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 243824
93.6%
1 16777
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 243824
93.6%
1 16777
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 243824
93.6%
1 16777
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 243824
93.6%
1 16777
 
6.4%

has_secondary_use_hotel
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
0
251838 
1
 
8763

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 251838
96.6%
1 8763
 
3.4%

Length

2024-02-02T10:51:15.636838image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-02T10:51:15.838713image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 251838
96.6%
1 8763
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 251838
96.6%
1 8763
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 251838
96.6%
1 8763
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 251838
96.6%
1 8763
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 251838
96.6%
1 8763
 
3.4%

has_secondary_use_rental
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
0
258490 
1
 
2111

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 258490
99.2%
1 2111
 
0.8%

Length

2024-02-02T10:51:15.979723image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-02T10:51:16.127042image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 258490
99.2%
1 2111
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 258490
99.2%
1 2111
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 258490
99.2%
1 2111
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 258490
99.2%
1 2111
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 258490
99.2%
1 2111
 
0.8%

has_secondary_use_institution
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
0
260356 
1
 
245

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 260356
99.9%
1 245
 
0.1%

Length

2024-02-02T10:51:16.321096image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-02T10:51:16.447366image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 260356
99.9%
1 245
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 260356
99.9%
1 245
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 260356
99.9%
1 245
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 260356
99.9%
1 245
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 260356
99.9%
1 245
 
0.1%

has_secondary_use_school
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
0
260507 
1
 
94

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 260507
> 99.9%
1 94
 
< 0.1%

Length

2024-02-02T10:51:16.586017image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-02T10:51:16.716961image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 260507
> 99.9%
1 94
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 260507
> 99.9%
1 94
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 260507
> 99.9%
1 94
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 260507
> 99.9%
1 94
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 260507
> 99.9%
1 94
 
< 0.1%

has_secondary_use_industry
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
0
260322 
1
 
279

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 260322
99.9%
1 279
 
0.1%

Length

2024-02-02T10:51:16.854991image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-02T10:51:16.988215image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 260322
99.9%
1 279
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 260322
99.9%
1 279
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 260322
99.9%
1 279
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 260322
99.9%
1 279
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 260322
99.9%
1 279
 
0.1%

has_secondary_use_health_post
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
0
260552 
1
 
49

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 260552
> 99.9%
1 49
 
< 0.1%

Length

2024-02-02T10:51:17.125923image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-02T10:51:17.266088image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 260552
> 99.9%
1 49
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 260552
> 99.9%
1 49
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 260552
> 99.9%
1 49
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 260552
> 99.9%
1 49
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 260552
> 99.9%
1 49
 
< 0.1%

has_secondary_use_gov_office
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
0
260563 
1
 
38

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 260563
> 99.9%
1 38
 
< 0.1%

Length

2024-02-02T10:51:17.399249image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-02T10:51:17.526770image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 260563
> 99.9%
1 38
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 260563
> 99.9%
1 38
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 260563
> 99.9%
1 38
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 260563
> 99.9%
1 38
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 260563
> 99.9%
1 38
 
< 0.1%

has_secondary_use_use_police
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
0
260578 
1
 
23

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 260578
> 99.9%
1 23
 
< 0.1%

Length

2024-02-02T10:51:17.662671image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-02T10:51:17.805926image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 260578
> 99.9%
1 23
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 260578
> 99.9%
1 23
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 260578
> 99.9%
1 23
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 260578
> 99.9%
1 23
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 260578
> 99.9%
1 23
 
< 0.1%

has_secondary_use_other
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
0
259267 
1
 
1334

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 259267
99.5%
1 1334
 
0.5%

Length

2024-02-02T10:51:17.943706image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-02T10:51:18.075729image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 259267
99.5%
1 1334
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 259267
99.5%
1 1334
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 259267
99.5%
1 1334
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 259267
99.5%
1 1334
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 259267
99.5%
1 1334
 
0.5%

damage_grade
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.0 MiB
2
148259 
3
87218 
1
25124 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters260601
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row3
4th row2
5th row3

Common Values

ValueCountFrequency (%)
2 148259
56.9%
3 87218
33.5%
1 25124
 
9.6%

Length

2024-02-02T10:51:18.216299image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-02T10:51:18.351844image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
2 148259
56.9%
3 87218
33.5%
1 25124
 
9.6%

Most occurring characters

ValueCountFrequency (%)
2 148259
56.9%
3 87218
33.5%
1 25124
 
9.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260601
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 148259
56.9%
3 87218
33.5%
1 25124
 
9.6%

Most occurring scripts

ValueCountFrequency (%)
Common 260601
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 148259
56.9%
3 87218
33.5%
1 25124
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 260601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 148259
56.9%
3 87218
33.5%
1 25124
 
9.6%

Interactions

2024-02-02T10:50:57.655255image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:44.904373image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:46.814917image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:48.774536image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:50.804448image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:52.732835image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:54.337597image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:56.202522image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:57.895014image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:45.142830image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:47.065983image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:48.950684image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:51.063967image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:52.900706image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:54.523821image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:56.412430image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:58.165040image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:45.494504image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:47.331871image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:49.223828image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:51.321300image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:53.079457image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:54.707717image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:56.584310image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:58.347228image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:45.716176image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:47.546339image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:49.480682image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:51.572844image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:53.275767image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:54.964424image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:56.752777image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:58.534752image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:45.897518image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:47.816662image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:49.752940image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:51.789391image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:53.523184image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:55.233563image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:56.928066image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:58.780489image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:46.156852image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:48.062671image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:50.008047image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:52.052995image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:53.778777image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:55.480554image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:57.085410image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:59.193706image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:46.424037image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:48.306946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:50.307727image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:52.308456image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:53.966560image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:55.727004image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:57.243205image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:59.436881image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:46.619956image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:48.556653image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:50.567892image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:52.556060image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:54.182446image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:55.957294image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-02T10:50:57.404706image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-02-02T10:50:59.884372image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-02T10:51:01.350468image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

geo_level_1_idgeo_level_2_idgeo_level_3_idcount_floors_pre_eqagearea_percentageheight_percentageland_surface_conditionfoundation_typeroof_typeground_floor_typeother_floor_typepositionplan_configurationhas_superstructure_adobe_mudhas_superstructure_mud_mortar_stonehas_superstructure_stone_flaghas_superstructure_cement_mortar_stonehas_superstructure_mud_mortar_brickhas_superstructure_cement_mortar_brickhas_superstructure_timberhas_superstructure_bamboohas_superstructure_rc_non_engineeredhas_superstructure_rc_engineeredhas_superstructure_otherlegal_ownership_statuscount_familieshas_secondary_usehas_secondary_use_agriculturehas_secondary_use_hotelhas_secondary_use_rentalhas_secondary_use_institutionhas_secondary_use_schoolhas_secondary_use_industryhas_secondary_use_health_posthas_secondary_use_gov_officehas_secondary_use_use_policehas_secondary_use_otherdamage_grade
building_id
80290664871219823065trnfqtd11000000000v1000000000003
288308900281221087ornxqsd01000000000v1000000000002
9494721363897321055trnfxtd01000000000v1000000000003
590882224181069421065trnfxsd01000011000v1000000000002
20194411131148833089trnfxsd10000000000v1000000000003
3330208558608921095trnfqsd01000000000v1110000000002
72845194751206622534nrnxqsd01000000000v1000000000003
47551520323122362086twqvxsu00000110000v1000000000001
4411260757721921586trqfqsd01000010000v1000000000002
9895002688699410134tinvjsd00000100000v1000000000001
geo_level_1_idgeo_level_2_idgeo_level_3_idcount_floors_pre_eqagearea_percentageheight_percentageland_surface_conditionfoundation_typeroof_typeground_floor_typeother_floor_typepositionplan_configurationhas_superstructure_adobe_mudhas_superstructure_mud_mortar_stonehas_superstructure_stone_flaghas_superstructure_cement_mortar_stonehas_superstructure_mud_mortar_brickhas_superstructure_cement_mortar_brickhas_superstructure_timberhas_superstructure_bamboohas_superstructure_rc_non_engineeredhas_superstructure_rc_engineeredhas_superstructure_otherlegal_ownership_statuscount_familieshas_secondary_usehas_secondary_use_agriculturehas_secondary_use_hotelhas_secondary_use_rentalhas_secondary_use_institutionhas_secondary_use_schoolhas_secondary_use_industryhas_secondary_use_health_posthas_secondary_use_gov_officehas_secondary_use_use_policehas_secondary_use_otherdamage_grade
building_id
56080520368598012553nrnfjsd01000000000v1110000000003
207683101382190322555trnfqsd01000010000v1000000000002
2264218767861325135trnfqsd01000000000v1110000000002
159555271811537601312trnfxjd00001000000v1000000000002
8270128268471822085trnfqsd01000000000v1000000000003
688636251335162115563nrnfjsq01000000000v1000000000002
6694851771520602065trnfqsd01000000000v1000000000003
6025121751816335567trqfqsd01000000000v1000000000003
15140926391851210146trxvsjd00000100000v1000000000002
747594219910131076nrnfqjd01000000000v3000000000003

Duplicate rows

Most frequently occurring

geo_level_1_idgeo_level_2_idgeo_level_3_idcount_floors_pre_eqagearea_percentageheight_percentageland_surface_conditionfoundation_typeroof_typeground_floor_typeother_floor_typepositionplan_configurationhas_superstructure_adobe_mudhas_superstructure_mud_mortar_stonehas_superstructure_stone_flaghas_superstructure_cement_mortar_stonehas_superstructure_mud_mortar_brickhas_superstructure_cement_mortar_brickhas_superstructure_timberhas_superstructure_bamboohas_superstructure_rc_non_engineeredhas_superstructure_rc_engineeredhas_superstructure_otherlegal_ownership_statuscount_familieshas_secondary_usehas_secondary_use_agriculturehas_secondary_use_hotelhas_secondary_use_rentalhas_secondary_use_institutionhas_secondary_use_schoolhas_secondary_use_industryhas_secondary_use_health_posthas_secondary_use_gov_officehas_secondary_use_use_policehas_secondary_use_otherdamage_grade# duplicates
45981013826532568trnfqsd01000000000v100000000000329
638917930898432067trnfqsd01000011000v100000000000315
9402272691112121587trnfxsd01000000000v100000000000315
731120863857821097trqxxsd01000010000v100000000000214
1099412181053121065trnfqsd01000000000v100000000000212
35541070942921054trnfqsd01000000000v100000000000212
86472639112461063tuqvjsq00000100000a100000000000112
94062726911121215107trnfxsd01000000000v100000000000312
319681114800221565nrnfqsd01000000000v100000000000311
3243720691722075trnfqsd01000000000v100000000000310